# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Implementation of fully connected network."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import tensorflow as tf


class FeedFowardNetwork(tf.layers.Layer):
    """Fully connected feedforward network."""

    def __init__(
        self, hidden_size, filter_size, relu_dropout, train, allow_pad
    ):
        super(FeedFowardNetwork, self).__init__()
        self.hidden_size = hidden_size
        self.filter_size = filter_size
        self.relu_dropout = relu_dropout
        self.train = train
        self.allow_pad = allow_pad

        self.filter_dense_layer = tf.layers.Dense(
            filter_size,
            use_bias = True,
            activation = tf.nn.relu,
            name = 'filter_layer',
        )
        self.output_dense_layer = tf.layers.Dense(
            hidden_size, use_bias = True, name = 'output_layer'
        )

    def call(self, x, padding = None):
        """Return outputs of the feedforward network.

    Args:
      x: tensor with shape [batch_size, length, hidden_size]
      padding: (optional) If set, the padding values are temporarily removed
        from x (provided self.allow_pad is set). The padding values are placed
        back in the output tensor in the same locations.
        shape [batch_size, length]

    Returns:
      Output of the feedforward network.
      tensor with shape [batch_size, length, hidden_size]
    """
        padding = None if not self.allow_pad else padding

        # Retrieve dynamically known shapes
        batch_size = tf.shape(x)[0]
        length = tf.shape(x)[1]

        if padding is not None:
            with tf.name_scope('remove_padding'):
                # Flatten padding to [batch_size*length]
                pad_mask = tf.reshape(padding, [-1])

                nonpad_ids = tf.to_int32(tf.where(pad_mask < 1e-9))

                # Reshape x to [batch_size*length, hidden_size] to remove padding
                x = tf.reshape(x, [-1, self.hidden_size])
                x = tf.gather_nd(x, indices = nonpad_ids)

                # Reshape x from 2 dimensions to 3 dimensions.
                x.set_shape([None, self.hidden_size])
                x = tf.expand_dims(x, axis = 0)

        output = self.filter_dense_layer(x)
        if self.train:
            output = tf.nn.dropout(output, 1.0 - self.relu_dropout)
        output = self.output_dense_layer(output)

        if padding is not None:
            with tf.name_scope('re_add_padding'):
                output = tf.squeeze(output, axis = 0)
                output = tf.scatter_nd(
                    indices = nonpad_ids,
                    updates = output,
                    shape = [batch_size * length, self.hidden_size],
                )
                output = tf.reshape(
                    output, [batch_size, length, self.hidden_size]
                )
        return output
